Pediatrics

An integrated approach to a predictive and ranking model of use error using fuzzy BWM and fuzzy TOPSIS.

Avoiding error in handling artifacts is crucial for achieving a high level of system reliability and safety assessment. This study develops a predictive and ranking model of use error (PRUE). In the first phase, use errors are systematically detected and anticipated via an inquiry process in two levels (activity and function). In the second phase, the fuzzy best-worst method (F-BWM) is employed to...

Development of an Artificial Intelligence-Based Algorithm for the Assessment of Skeletal Age and Detection of Cervical Vertebral Anomalies in Patients with Cleft Lip and Palate.

ObjectiveTo develop an artificial intelligence (AI)-based algorithm for the assessment and comparison of skeletal maturation in patients with and without cleft lip and/or palate and to detect the presence of cervical vertebral anomalies (CVAs).DesignRetrospective cohort study.SettingA university orthodontic clinic and comprehensive cleft care centers.Patients/ParticipantsIn total, 1080 cephalogram...

CDSNet: An automated method for assessing growth stages from various anatomical regions in lateral cephalograms based on deep learning.

BACKGROUND: The assessment of growth stages, typically determined by Cervical Vertebrae Maturation (CVM), plays a crucial role in orthodontics. Howeve...

Classifying vocal responses of broilers to environmental stressors via artificial neural network.

Detecting early-stage stress in broiler farms is crucial for optimising growth rates and animal well-being. This study aims to classify various stress...

Computer-aided diagnosis of early-stage Retinopathy of Prematurity in neonatal fundus images using artificial intelligence.

Retinopathy of Prematurity (ROP) is a retinal disorder affecting preterm babies, which can lead to permanent blindness without treatment. Early-stage ...

Prioritization of monitoring compounds from SNTS identified organic micropollutants in contaminated groundwater using a machine learning optimized ToxPi model.

Advanced suspect and non-target screening (SNTS) approach can identify a large number of potential hazardous micropollutants in groundwater, underscor...

The Future of Surgical Diagnostics: Artificial Intelligence-Enhanced Detection of Ganglion Cells for Hirschsprung Disease.

Hirschsprung disease, a congenital disease characterized by the absence of ganglion cells, presents significant surgical challenges. Addressing a crit...

Prediction of Survival After Pediatric Cardiac Arrest Using Quantitative EEG and Machine Learning Techniques.

BACKGROUND AND OBJECTIVES: Early neuroprognostication in children with reduced consciousness after cardiac arrest (CA) is a major clinical challenge. ...

Using machine learning to identify pediatric ophthalmologists.

This cross-sectional study used data from the American Academy of Ophthalmology IRIS Registry (Intelligent Research in Sight) and machine learning alg...

Recent advances in culture medium design for enhanced production of monoclonal antibodies in CHO cells: A comparative study of machine learning and systems biology approaches.

The production of monoclonal antibodies (mAbs) using Chinese Hamster Ovary (CHO) cells has revolutionized the treatment of numerous diseases, solidify...

Using Artificial Intelligence to Identify Three Presenting Phenotypes of Chiari Type-1 Malformation and Syringomyelia.

BACKGROUND AND OBJECTIVES: Chiari type-1 malformation (CM1) and syringomyelia (SM) are common related pediatric neurosurgical conditions with heteroge...

Artificial intelligence in assessing progression of age-related macular degeneration.

The human population is steadily growing with increased life expectancy, impacting the prevalence of age-dependent diseases, including age-related mac...

Whole Slide Imaging, Artificial Intelligence, and Machine Learning in Pediatric and Perinatal Pathology: Current Status and Future Directions.

The integration of artificial intelligence (AI) into healthcare is becoming increasingly mainstream. Leveraging digital technologies, such as AI and d...

Artificial intelligence: a primer for pediatric radiologists.

Artificial intelligence (AI) is increasingly recognized for its transformative potential in radiology; yet, its application in pediatric radiology is ...

Random Forest Prognostication of Survival and 6-Month Outcome in Pediatric Patients Following Decompressive Craniectomy for Traumatic Brain Injury.

BACKGROUND: There is a dearth of literature regarding prognostic and predictive factors for outcome following pediatric decompressive craniectomy (DC)...

AI for Analyzing Mental Health Disorders Among Social Media Users: Quarter-Century Narrative Review of Progress and Challenges.

BACKGROUND: Mental health disorders are currently the main contributor to poor quality of life and years lived with disability. Symptoms common to man...

AFSleepNet: Attention-Based Multi-View Feature Fusion Framework for Pediatric Sleep Staging.

The widespread prevalence of sleep problems in children highlights the importance of timely and accurate sleep staging in the diagnosis and treatment ...

Deep learning-based models for preimplantation mouse and human embryos based on single-cell RNA sequencing.

The rapid growth of single-cell transcriptomic technology has produced an increasing number of datasets for both embryonic development and in vitro pl...

Machine learning algorithms for prediction of measles one vaccination dropout among 12-23 months children in Ethiopia.

INTRODUCTION: Despite the availability of a safe and effective measles vaccine in Ethiopia, the country has experienced recurrent and significant meas...

Analysis of the impact of deep learning know-how and data in modelling neonatal EEG.

The performance gains achieved by deep learning models nowadays are mainly attributed to the usage of ever larger datasets. In this study, we present ...